PPT-Intro to GPU s for Parallel Computing

Goals for Rest of Course Learn how to program massively parallel processors and achieve high performance functionality and maintainability scalability across future generations Acquire technical knowledge required to achieve the above goals. Dr A . Sahu. Dept of Comp Sc & . Engg. . . IIT . Guwahati. 1. Outline. Graphics System . GPU Architecture. Memory Model. Vertex Buffer, Texture buffer. GPU Programming Model. DirectX. , OpenGL, . Goals for Rest of Course. Learn how to program massively parallel processors and achieve. high performance. functionality and maintainability. scalability across future generations. Acquire technical knowledge required to achieve the above goals. ITS Research Computing. Lani. Clough, Mark Reed. markreed@unc.edu. . Objectives. Introductory. level MATLAB course for people who want to learn . parallel and GPU computing . in MATLAB.. Help participants . mei. W. . Hwu. , 2007-2012 . University . of Illinois, Urbana-Champaign. 1. CS/EE 217. GPU Architecture and Parallel . Programming. Project . Kickoff. Two flavors. Application. Implement/optimize an realistic application on GPGPUs. using BU Shared Computing Cluster. Scientific Computing and Visualization. Boston . University. GPU Programming. GPU – graphics processing unit. Originally designed as a graphics processor. Nvidia's. Lecture 2: more basics. Recap. Can use GPU to solve highly parallelizable problems. Straightforward extension to C++. Separate CUDA code into .cu and .. cuh. files and compile with . nvcc. to create object files (.o files). Going Beyond Serial MATLAB Applications. MATLAB . Desktop (Client). Worker. Worker. Worker. Worker. Worker. Worker. Programming Parallel Applications (CPU). Built-in support. with t. oolboxes. Ease of Use. Lecture 2: more basics. Recap. Can use GPU to solve highly parallelizable problems. Straightforward extension to C++. Separate CUDA code into .cu and .. cuh. files and compile with . nvcc. to create object files (.o files). Add GPUs: Accelerate Science Applications. © NVIDIA 2013. Small Changes, Big Speed-up. Application Code. . GPU. C. PU. Use GPU to Parallelize. Compute-Intensive Functions. Rest of Sequential. CPU Code. Eileen Berman (stealing from many people). DUNE Collaboration Meeting. Jan 29, 2018. What Does This Include?. Getting Started. LArSoft. . (thanks to Erica Snider). Gallery. . (thanks to Marc . Paterno. Single machine, multi-core. P(OSIX) threads: bare metal multi-threading. OpenMP. :. compiler directives that implement various constructs like parallel-for. Single machine, GPU. CUDA/. OpenCL. :. bare metal GPU coding. Research Computing Services. Boston . University. GPU Programming. Access to the SCC. Login: . tuta#. Password: . VizTut#. GPU Programming. Access to the SCC GPU nodes. # copy tutorial materials: . Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2014. Lectures. Monday. 6-9pm. Moore 212. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures. Image from . Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2013. Lectures. Monday and Wednesday. 6-7:30pm. Towne . 307. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures.